






"""simple docstring"""
class      UpperCamelCase_			(__A	):
	pass







class      UpperCamelCase_			(__A	):
	pass







class      UpperCamelCase_			:





	def __init__( self   :					List[str]		)		->     Tuple:
				UpperCAmelCase_	:					int     	=   [
				    [],
				    [],
				    [],
				]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int		)		->     None:
				try:
							if len(self.queues[priority]		) >= 100:
										raise OverflowError("Maximum queue size is 100"		)
							self.queues[priority].append(lowerCAmelCase_		)
				except IndexError:
							raise ValueError("Valid priorities are 0, 1, and 2"		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				for queue in self.queues:
							if queue:
										return queue.pop(0		)
				raise UnderFlowError("All queues are empty"		)





	def __str__( self   :					int		)		->     str:
				return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues		)		)







class      UpperCamelCase_			:





	def __init__( self   :					int		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Tuple     	=   []





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					int		)		->     None:
				if len(self.queue		) == 100:
							raise OverFlowError("Maximum queue size is 100"		)
				self.queue.append(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				if not self.queue:
							raise UnderFlowError("The queue is empty"		)
				else:
							UpperCAmelCase_	:					Union[str, Any]     	=   min(self.queue		)
							self.queue.remove(lowerCAmelCase_		)
							return data





	def __str__( self   :					Dict		)		->     str:
				return str(self.queue		)





def 							snake_case (  ):
			UpperCAmelCase_	:					Dict     	=   FixedPriorityQueue()
			fpq.enqueue(0      ,10		)
			fpq.enqueue(1      ,70		)
			fpq.enqueue(0      ,1_00		)
			fpq.enqueue(2      ,1		)
			fpq.enqueue(2      ,5		)
			fpq.enqueue(1      ,7		)
			fpq.enqueue(2      ,4		)
			fpq.enqueue(1      ,64		)
			fpq.enqueue(0      ,1_28		)
			print(A__		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(A__		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)
			print(fpq.dequeue()		)

def 							snake_case (  ):
			UpperCAmelCase_	:					Dict     	=   ElementPriorityQueue()
			epq.enqueue(10		)
			epq.enqueue(70		)
			epq.enqueue(1_00		)
			epq.enqueue(1		)
			epq.enqueue(5		)
			epq.enqueue(7		)
			epq.enqueue(4		)
			epq.enqueue(64		)
			epq.enqueue(1_28		)
			print(A__		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(A__		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)
			print(epq.dequeue()		)


if __name__ == "__main__":
			fixed_priority_queue()
			element_priority_queue()




from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)